A neural network algorithm for solving the traffic control problem in multistage interconnection networks

The authors propose a neural network algorithm for the traffic control problem (an NP-complete problem) in multistage interconnection networks. The traffic control problem can be represented by an energy function, and the state of the energy function is iteratively updated by the authors´ parallel algorithm. When the energy function reaches a stable state, the state represents a solution of the problem. Empirical results show the effectiveness of the proposed algorithm, and the time complexity with n² neurons is O(n log n ). Simulation results show that both the throughput and iteration steps are much better than in the linear approach. Furthermore, since the traffic control problem can be reduced to the traveling salesman problem. the proposed algorithm can also be applied to other optimization problems